Why this matters
AI products succeed when your bets are sequenced, resourced, and de-risked. Roadmap and portfolio planning helps you decide what to build now, what to test next, and what to defer—while aligning with compliance, data readiness, and measurable outcomes.
- Translate strategy into timed outcomes and experiments.
- Balance high-impact bets with near-term wins across teams.
- Plan for data work, evaluation, and responsible AI gates—not just features.
- Make trade-offs visible: value vs. risk vs. feasibility.
Who this is for and prerequisites
Who this is for
- AI Product Managers and Product Leaders sequencing multiple AI initiatives.
- Data Science leads and Tech PMs coordinating platform, data, and model work.
- Founders and PMs making outcome-driven investment choices.
Prerequisites
- Basic product strategy (outcomes, KPIs, OKRs).
- Intro to ML lifecycle (data, training, evaluation, deployment, monitoring).
- Familiarity with responsible AI concepts (bias, privacy, safety).
Concept explained simply
An AI roadmap is a timed plan of outcomes, experiments, and releases. A portfolio view shows all your AI bets together, so you can balance risk, impact, and capacity across teams.
Mental model
- Now / Next / Later: simple horizons for decision speed.
- Triangle of Value–Risk–Feasibility: choose the best trade-off, not just the biggest idea.
- Gates, not dates: quality and safety milestones (e.g., offline eval thresholds) that must be met before moving forward.
Key components of an AI roadmap
- Clear outcomes: measurable, time-bound, business-linked (e.g., reduce handling time by 15%).
- Hypotheses and experiments: what you must learn to reduce risk.
- Dependencies: data access/quality, platform readiness, privacy/compliance reviews.
- Evaluation plan: offline metrics, human evaluation, A/B guardrails, drift monitoring.
- Release stages: pilot, limited GA, GA; with responsible AI gates.
- Capacity plan: who is needed when (data, ML, platform, legal, design, support).
- Decision cadence: monthly reviews to add/drop/resize bets.
Worked examples
Example 1: Quarterly AI roadmap for a Support Assistant
Outcome target: Reduce average handle time (AHT) by 15% in two quarters.
- Q1 (Now):
- Data readiness: label 5k conversations; PII redaction pipeline.
- Prototype: retrieval-augmented generation (RAG) with offline eval ≥ 0.75 answer quality.
- Responsible AI gate: bias review on top 5 intents; incident response doc draft.
- Q2 (Next):
- Pilot: 10% traffic; success: +8% AHT reduction; fallbacks defined.
- Improve retrieval and prompts; add human feedback loop.
- Decision: if pilot meets gates, ramp to 50% with guardrails.
Example 2: Portfolio balancing across three teams
Initiatives:
- Exploit (low risk, near-term): smart auto-replies in app (impact medium, effort low).
- Expand (moderate risk): churn prediction uplift (impact high, effort medium).
- Explore (high risk): speech-to-insights for sales calls (impact potentially very high, effort high).
Balance target for quarter: 50% Exploit, 35% Expand, 15% Explore. Capacity fit: Team A (platform-heavy), Team B (DS-heavy), Team C (experimentation). Result: Assign Exploit to B, Expand to A+B shared, Explore to C with capped timebox and clear kill criteria.
Example 3: Scenario capacity plan with compliance gates
Assume 12 team-weeks per month across Data (4), ML (4), Platform (4). Compliance review requires 2 weeks from Legal/Privacy per major release.
- Scenario A (baseline): hit GA in Month 3 with 2 pilots in Month 2.
- Scenario B (regulated market): add 1 extra month for privacy review and human evaluation. Adjust roadmap: shift GA to Month 4; keep outcomes stable but extend pilot period.
- Decision: choose B for EU launch; reassign Platform capacity to reliability in Month 3 to avoid idle time.
Step-by-step: build an AI roadmap
- Define outcomes and guardrails
- 1–2 primary metrics (business), 1 safety metric (e.g., hallucination rate).
- Exit criteria for each release stage.
- Map dependencies
- Data sources, labeling, privacy, infra, human evaluation, support training.
- Prioritize initiatives
- Use RICE/ICE plus risk discount for model uncertainty.
- Allocate capacity and timebox experiments
- Set Explore/Expand/Exploit targets; impose kill/sustain thresholds.
- Set review cadence
- Monthly portfolio review; weekly delivery check; update risks.
Checklist: before you publish the roadmap
- Outcomes and safety gates are explicit and measurable.
- Data readiness plan exists (labeling, quality, access, privacy).
- Experiments have timeboxes and success thresholds.
- Dependencies and owners are clear.
- Capacity matches the plan by skill (Data, ML, Platform, Compliance).
- Rollback and incident plans are prepared.
Exercises
Try these mini tasks. Compare your work with the solutions in the exercise section below.
- Exercise 1 (ex1): Create a two-quarter AI roadmap for a single product using Now/Next/Later, outcomes, and gates.
- Exercise 2 (ex2): Build a portfolio balance across at least five initiatives and recommend what to drop, defer, and accelerate.
Self-check before viewing solutions
- Did you define outcomes and safety gates for each stage?
- Did you account for data and compliance dependencies?
- Is capacity by function realistic, with buffers?
- Are kill criteria explicit for exploratory bets?
Common mistakes and how to self-check
- Only listing features, not outcomes — rewrite each item as an outcome + metric.
- Ignoring data work — add data tasks (labeling, quality, governance) to the plan.
- No quality or safety gates — define offline eval, human eval, and rollback.
- Overcommitting capacity — reserve 20–30% for integration and unexpected work.
- Skipping compliance — schedule privacy/security/ethics reviews before pilots.
- One-way plan — set monthly decision points to stop/continue/reshape.
Quick self-audit
- Can you pause a bet in one meeting with your current gates?
- Is any item blocked by a dependency you did not schedule?
- Would a new risk (e.g., model drift) change your next two weeks?
Practical projects
- Project 1: Publish a one-page AI roadmap with Now/Next/Later, outcomes, gates, and owners. Review with stakeholders.
- Project 2: Build a portfolio board (Explore/Expand/Exploit) for all AI initiatives. Set balance targets and kill criteria.
- Project 3: Create a release playbook: offline eval thresholds, human eval script, safety review checklist, rollback plan.
Learning path
- Product outcomes and metrics (OKRs, North Star metrics).
- ML lifecycle and MLOps basics (data, evaluation, deployment, monitoring).
- Responsible AI and compliance (privacy, bias, safety, auditability).
- Experiment design and analysis (A/B tests, offline vs. online evaluation).
- Stakeholder communication and decision forums (reviews, trade-off docs).
Next steps
- Draft your Now/Next/Later with outcomes and gates.
- Run a 30-minute dependency mapping session with engineering and data.
- Schedule a monthly portfolio review and define stop/continue rules.
Mini challenge
Your CEO wants a generative AI assistant launched in two months. Data is messy; privacy review takes three weeks. In 5–7 bullet points, craft a minimal roadmap that hits a small, safe win in two months while setting up for scale. Include at least one kill criterion and one safety gate.
Quick test
The quick test below is available to everyone. If you log in, your progress will be saved.